Artificial Intelligence-Driven Digital Technologies on SDG

Ver.	Summary	Created by	Modification	Content Size	Created at	Operation
1		Angélica Pigola	+ 3470 word(s)	3470	2021-12-16 02:56:30	\|
2	format correct	Conner Chen	Meta information modification	3470	2021-12-20 03:32:04	\| \|
3	Remove from the EC "Structures for Engineering and Architecture"	Lindsay Dong	Meta information modification	3470	2022-03-28 05:40:10	\|

This entry is adapted from the peer-reviewed paper 10.3390/su132413669

Artificial Intelligence-Driven (AI-Driven) digital technologies (DT) are intrinsically connected to interact, perceive, and understand people, businesses, economies, and lives in general. The term Artificial Intelligence (AI) can be understood as a general combination and integration of applications with other “DTs” to create machines capable of thinking like humans. AI-Driven DT economic and societal impacts increase on a continuous basis and more recently they are assuming an important role in the Sustainable Development Goals (SDG) Agenda 2030, and their implementations are a considerable decision for developed and developing countries. In turn, Brazil and Portugal have been elected in this research to display their view on AI-driven DT on SDG achievements, contradicting their perspectives in this field.

digital technologies artificial intelligence sustainable development goals

1. Introduction

Artificial Intelligence-Driven (AI-Driven) digital technologies (DT) are intrinsically connected to interact, perceive, and understand people, businesses, economies, and lives in general ^[1]. The term Artificial Intelligence (AI) can be understood as a general combination and integration of applications with other “DTs” to create machines capable of thinking like humans ^[1]^[2]^[3]. AI-Driven DT economic and societal impacts increase on a continuous basis and more recently they are assuming an important role in the Sustainable Development Goals (SDG) Agenda 2030 ^[4], and their implementations are a considerable decision for developed and developing countries. In turn, Brazil and Portugal have been elected in this research to display their view on AI-driven DT on SDG achievements, contradicting their perspectives in this field.

As we enter into the age of sustainable development ^[5], in which the 17 SDGs (discussed in Section 2) are guiding nations of the world, Artificial Intelligence (AI) and digital technologies (DT)—such as digital twins, blockchain, virtual and augmented reality, and big data, among others—create a high expectation in enhancing economic, environment, and societal levels in global transformation to attend to SDGs ^[1]. Some priorities or key elements are pointed out by Palomares et al. (2021) to have optimal results and impacts on AI usage on the SDGs, such as (i) AI-driven DT fueled by universally accessible and reliable data; (ii) strengthen science–industry–government dialogue for technology and knowledge transferring; (iii) adapt and coordinate action plans in each country and context; (iv) alternative standards for facilitating the evaluation of SDG attainment; and (v) lessons learnt since the pandemic ^[1].

Numerous studies also identified advantages in economic and environmental evaluation by an AI-Driven DT application, leveraging cleaner production—mainly in industry segments ^[6]^[7]^[8]^[9]—for more productive processes; less toxic and more biodegradable products; and increased efficiency of raw materials, water, and energy, aiming at non-generation, reduction, or recycling of waste ^[7]. According to Oliveira Neto et al. ^[7], the environmental advantages from the adoption of cleaner production are concentrated and indirectly related to Industry, Innovation, and Infrastructure (SDG 9); Responsible Production and Consumption (SDG 12); and protection of Life on Land (SDG 15).

Due to a roller coaster of AI-driven DT success and failure in a consistent pattern over AI history ^[2]^[10]^[11], many countries pursue different strategies to reach SDG incorporating technologies in various forms through experimentation, public policies, triple helix, joint-ventures, global alliances, or SDG technological programs. Specialized professionals are involved in many of these initiatives to figure out solutions to apply AI-driven DT with a lower-cost impact to overcome country boundaries. In this vein, this study aims to compare the preference for artificial intelligence-driven digital technologies (AI-Driven DT) to achieve SDGs in Brazil and Portugal.

This study points out a ranking of preferable AI-Driven DT for the different SDGs in these two countries—Brazil and Portugal—conducting a public survey describing and classifying a set of technologies recommended for each SDG. The respondents are experts, executives involved in sustainability, and interested people who classified some AI-driven DT considered the most appropriate to reach SDG goals.

A final analysis is performed for each SDG along with preferred AI-Driven DT comparing similarities and discrepancies between these countries, with special attention on SDG 9, 12, and 15, which are related to cleaner production. Brazil and Portugal have different preferences in terms of SDG priorities and AI-Driven DT, with the former prioritizing DT that supports education aspects and the latter prioritizing those that support small and medium companies.

2. AI-Driven DTs and Sustainable Development Goals

In this section, we introduce a brief description of AI-Driven DT into the 17 SDGs proposed by the United Nations Agenda 2030 ^[4] under three dimension perspectives ^[1]^[3] and their contributions and barriers to attain the different aspects of each SDG. In a sequence, we present a panorama of Brazil and Portugal and their efforts to achieve SDGs.

2.1. Contributions and Barriers of AI-Driven DT on SDG

The AI definition ^[12], from a pragmatic stand point, was presented by some authors as a computational natural language process capable of communicating with human beings through visual information, reasonable knowledge representation for decision-making, mind-thinking automated to process knowledge stored, and machine learning to extract common patterns from the available information. However, AI itself is not enough because it depends on data flow to learn and demands from other DTs for proper performance.

Therefore, AI-driven DT also faces challenges related to fairness, accountability, transparency, and ethics (FATE) for a reliable development, and the aim is to provide robustness to systems to avoid damages, lawfulness required by regulations and laws, and ethicalness to respect freedom, dignity, equality, citizen-rights, or non-discrimination principles ^[3]^[13].

Many of these are AI systems connected with DTs. Then, we briefly point out some of those that are used as DTs in the questionnaire applied in this study: Internet of Things (IoT), Blockchain, Augmented Reality, Virtual Reality, Digital Twin, 5G Communication Infrastructure, Big Data, and Recommender and Information Systems.

The wealthiness of alternatives to solving problems through AI-Driven DTs may support countries in reaching SDGs rapidly; however, they may result in inequalities due to the restriction of educational and computing resources throughout the world—mainly in developing countries, among other challenges. Additionally, a wide range of DTs are being developed that affect individuals lives, as well as impact economic, environmental, and societal aspects, requiring piloting new approaches and procedures from governments on the purpose of achieving SDGs ^[14].

Contributions and barriers for AI-driven DT are evidenced by some authors in the literature ^[1]^[2]^[3]^[14]; thus, we present in Table 1 a summary of the AI-Driven DT analysis based on the categorization among the SDGs, presented by Palomares et al. ^[1], relating to economy, environmental, and societal aspects. We highlight the economic dimension as sustainability and individual welfare concentration at an economic level, considering the welfare itself and prosperity; at the environmental dimension, safeguarding and preserving the environment, as well as the sustainable management of resources; at the social dimension, a sustainable development regarding welfare, prosperity at the community level, and equality are considered ^[14]^[15].

Table 1. Brief contributions and barriers of AI-Driven DT usage on SDGs.

SDG	Contributions	Barriers
Economic Dimension
SDG1. No Poverty ^[1]^[16]^[17]	Data providing a deep learning process in a domestic income predictor, determining appropriate thresholds of poverty and its classification.	Dependence on other nations if no pathways for AI breakthroughs are identified nationwide. AI-driven automation could affect low-salary labor workforce.
SDG2. Zero Hungry ^[1]^[18]	Gathering socio-economic and demographic information to predict famine or demand after disasters or crop diseases and plagues.	Sharing big data to foster intelligent farming practices may be subject to the appropriation and abuse of such data.
SDG3. Health & wellbeing ^[1]^[3]^[19]	Using predictive machine learning for various medical prognosis and experts’ judgement in advances in biomedicine.	Ethical dilemmas about the culpability in fatal outcomes with AI usage or an excessive loss of human skills in medical or surgical procedures.
SDG8. Work & economic growth ^[1]^[20]	Efficient transportation and flexible working through smart cities, external outsourcing, and digital labor, creating employment or anticipating job accidents in risk contexts using ambient intelligence.	Non-regulated AI deployment in business contexts and workers replacements by robots or algorithms in developing countries are increasing inequalities; or psychological risks from remote working are rising.
SDG9. Industry, Innovation & Infrastructure ^[21]	Sustainable smart factories and inclusive innovation for developing regions. Supporting of SMEs and startups anywhere. Detection of anomalies and maintenance facilitation from remote computational vision and models.	Lack of scientific standards for some DT (i.e., Digital 3D and Digital Twins), lack of integrated data platforms hampers intelligent systems. Governments and companies’ reluctance to openly report pollutant emissions to build AI prediction and warning systems.
Social Dimension
SDG 11. Cities & Communities ^[1]^[20]^[21]^[22]	Technologies that monitor and predict new systems building, technologies to optimize essential cities’ supplies, or that preserve heritage and nature facilitating citizen lives.	Few citizen-centered initiatives, human behavior is unpredictable to be a data source, barriers from public and private institutions to achieve data interoperability.
SDG 16. Peace, justice & institutions ^[1]^[23]	Better decision-making processes based on data crime in real-time, crime prediction or crime diagnosis at little costs, and justice accessibility through higher community coverage.	Diversity compromised by globalized views, wrong usage of technologies aggravating security breaches, intentional manipulation causing bias against certain groups in crime prediction tasks.
SDG 17. Partnerships for the goals ^[1]^[24]^[25]	Citizen awareness towards a life shared, centered, and ethical vision of people, or partnerships to set global standards for sustainability for massive earth observation.	Ethical dilemmas and negative public reactions are difficult to evaluate and hinder the consolidation of digital standards and negative impacts on communities by algorithmic decisions.
SDG 4. Quality Education ^[1]^[26]^[27]	Student engagement with special needs, broader classroom participation promoting ideas that empower citizens, sharing contents that drive equality. Content for learners’ individual needs adapted in favor of inclusive education.	Insufficient training of technologies and user–computer interaction below the pace of digital transformation in education systems and society. Teachers without skills in DT. Inequality in technologies access, regarding they are not a universal right.
SDG 5. Gender Equality ^[1]^[3]^[25]	Women’s empowerment openness economic and psychologically, releasing them from men’s dependency. Raising co-operative awareness among women with common interests worldwide.	Privacy concerns and digital harassment in social media. Patriarchal family structures in some countries. Retaliation and government control against those who oppose the status quo. Job market losses for those without digital skills.
SDG 10: Reduced Inequalities ^[1]^[3]	Cyber-security technologies offering strategic view in manipulation detection of financial markets. Open opportunities for foreign trade by small firms at lower costs. Alleviating economic breach across workers in various sectors using financial recommender systems.	Difficulties for data generation mechanisms from discriminated communities to update systems. Automated work and economic environments accentuate inequalities against vulnerable individuals. Polarization across sectors enhanced through fake news and bots yield a dangerous trend.
Environmental Dimension
SDG 6. Clean water and sanitation ^[1]^[28]	Predicting weather and drought by planned, complex water system simplification, facilitating human intervention with real-time data to reduce contamination and assure quality. Water resource distribution fairness.	Shortage of high-quality data and complete information, high temporal variability in water-related processes, focus on short-term predictive models has disregarded advances in long-term reliable water predictions, and lack of staff jointly specialized in DT and water resources.
SDG 7. Affordable and clean Energy ^[6]^[17]^[29]	Safer management of renewable energy plants reducing energy consumption. Remote decentralized management of massive energy infrastructures in real-time. Energy efficiency and its timely supply at an optimal cost.	Cyber-attacks, long-term obsolescence, and no standardization of digital energy systems are vulnerabilities and cause difficulties of implementation. Digitization consumption tends to cause blackouts in developing countries, representing an expressive part of global energy consumption.
SDG 12. Responsible consumption and Production ^[7]^[30]	Accountability and transparency in consumption policies to predict and simulate production processes to reduce energy consumption and raw material overuse. Early detection of breakdown to prevent waste, more synergy of production and consumption, aiming reductions in industrial waste and pollutant emissions. Production planning adapted to predicted consumption patterns to avoid unnecessary waste.	High simplification of production chains for their optimization, resource availability dependence on weather factors affecting predictions, and hard production process adaptation due to high modification costs. Sustainability and cost reduction are often two opposite goals in industrial production. Deemed unacceptable costs of integrating AI and DT might be denied by firms and consumers.
SDG 13. Climate Action ^[1]^[31]^[32]	Remotely assist countries to make better emergency or disaster recovery decisions. Education of younger generations about climate change action. Early prediction of natural catastrophes, enabling loss reduction and better understanding of desertification trends.	Not affordable data or information in certain regions, certain political resistance, and economic cost for large-scale systems to optimize pollutant emissions in urban areas combined with inherent computational cost requires significant energy.
SDG 14. Life below water ^[1]^[33]^[34]	Predict water quality parameters, early oil dumping detection and ocean acidification estimation. Exploiting data from monitoring sources to obtain knowledge for predictive decision-making about sustainable exploitation of ocean resources.	Digitization incurs high economic costs. Massive volumes of data to make accurate estimates are difficult to obtain due to the complexity of the marine physical environment. Malicious uses of digital technologies, and cyber-attacks may lead to uncontrolled overexploitation.
SDG 15. Life on land ^[8]^[35]	Early disease detection in crops to reduce herbicide use and environmental impact. Sensor-driven automatic fire detection for earlier, safer action and cost reduction. Intelligent irrigation and automate cultivation, reducing water consumption. Wildlife and ecosystems protection. Farming product sales forecasting to prevent overproduction and waste.	Complexity of deploying highly sophisticated DT capable of operating in difficult conditions, e.g., low visibility due to fires. Cost of deploying in farmlands, not affordable to small farmers. AI to reduce deforestation is a major challenge in developed countries, implying logistic problems.

2.2. A Panoram of SDG in Brazil and Portugal

In this subsection, we present the main strategic initiatives from Brazil and Portugal to disseminate SDGs and move forward to new achievements in the SDG Agenda 2030.

2.2.1. Brazil Initiatives for SDG

In general, countries are failing to adopt SDGs around the globe ^[36]. Brazil has been progressing in the SDG Agenda 2030, particularly in the field of decent work and eco-nomic growth. Nonetheless, considering Brazil’s reality, sustainable development is still far away from Brazilian households. Poor existent sanitary infrastructure to handle plastics residuals, solid trash, sewage, and particularly kitchen oil which are deposited in many regions protected by national laws are some challenges faced. ^[37].

Ali et al. ^[36] pointed out that Brazilian companies are more focused on economic activities, development of institutions, and securing respectable work opportunities for their people. However, three SDG goals are not highlighted in the vision and mission statements of the Brazilian companies: ‘Quality Education’, ‘Gender Equality’, and ‘Life Below Water’.

In the scenario of diversity that defines Brazil, some strategies were defined as essential for SDG achievements: (i) national governance through the creation of the National Commission for the SDGs as an advisory and parity body; (ii) adequacy of targets to take into account regional diversity, Brazilian government priorities, national development plans, current legislation, and the socioeconomic situation experienced by the country; and (iii) national indicators considering data availability and monitoring ^[37].

In addition to the Brazilian government’s planning instruments ^[37], Brazil intends to stimulate the creation of local governance structures, which will lead the process of localizing the 2030 Agenda in the territories encompassing an engagement of private sector, academia and civil society organizations, preparation of monitoring reports, building institutional partnerships, preparation of a multi-year plan, creation of subnational commissions, SDG Brazil Award, and training public managers. Some tools by the initiative of the government and civil society have supported the planning and the dissemination of the SDGs, which are highlighted in Table 2.

Table 2. Brazilian initiatives for SDG dissemination.

Initiatives	Description	Access
Dialoga Brasil	A digital participation platform where citizens can make suggestions to assist in the debate and formulation of public policies including those to reach SDG targets.	http://dialoga.gov.br Accessed on 6 October 2021
SDG Strategy	An electronic website bringing together organizations representing civil society, the private sector, local governments, and academia, with the aim of broadening and enhancing the debate on SDG and mobilizing, discussing, and proposing means of implementation for the 2030 Agenda.	http://www.estrategiaods.org.br Accessed on 6 October 2021
Participa.br Portal	A social media instrument providing participation tools for citizens, networks, social movements, and organizations, enabling dialogue among governmental bodies and society, through public consultations, debates, conferences, and online events.	http://www.participa.br Accessed on 6 October 2021
The 2030 Agenda Platform	A platform structured into three axes: (i) information presenting the process of developing the follow-up agenda for the SDGs and their targets, as well as providing publications and contents on the 2030 Agenda in Brazil; (ii) monitoring and review, which provides information on the monitoring indicators and will present graphs and database with SDG outcomes in the federated entities; (iii) participation, whose main target audience comprises users and institutions wishing to follow up discussions and advances regarding the SDGs.	http://www.agenda2030.com.br Accessed on 6 October 2021
Map of Civil Society Organizations	A georeferenced platform with data on civil society organizations, allowing for the dissemination of the 2030 Agenda, as well as a follow-up of activities carried out by these organizations and their relationship with the respective SDG targets.	http://mapaosc.ipea.gov.br Accessed on 6 October 2021
Municipal Vulnerability Atlas	A platform comprising the Social Vulnerability Index (IVS), based on indicators of the Human Development Atlas1. Organized in three dimensions: Urban Infrastructure, Human Capital, and Income and Labor. The Social Vulnerability Index allows mapping out exclusion and social vulnerability in 5565 municipalities and in Human Development Units of the main metropolitan regions of the country. This tool assists municipalities to assess and plan actions focused on local.	http://ivs.ipea.gov.br Accessed on 6 October 2021

Source: Secretariat of Government of the Presidency of the Republic ^[33].

2.2.2. Portugal Initiatives for SDG

Portugal has followed up its SDG statistics through the Instituto Nacional de Estatística (INE) as the central institution for the production and dissemination of official statistics of Portugal. They have been in close coordination with other statistical departments of various ministries and national authorities, involved in the implementation of SDG strategic priorities to gather efforts in achieving the Agenda 2030 ^[38].

The country has been strongly involved in the efforts undertaken by other international bodies to align its respective policies and instruments to the SDG ambitions. In 2016 at the Conference of the Parties to the United Nations Framework Convention on Climate Change, Portugal took on the goal of achieving carbon neutrality by 2050, having developed a roadmap for carbon neutrality that set the vision, the trajectories, and guidelines for the policies to be implemented in this time frame.

The response to this challenge will be truly transformational in the way in which some of the most determining aspects of life in society face, regarding production and consumption patterns; the relationship with this production and use of energy; the way in which cities and spaces are organized for housing, work, and leisure; and the way mobility needs are faced with. In addition to being a technological challenge, this will also be a societal challenge that will depend considerably on the support and adhesion of the entire society.

Portugal embodies its strategic priorities ^[38] for the implementation of the 2030 Agenda for Sustainable Development in SDG4—Quality Education, SDG5—Gender Equality, SDG9—Industry, Innovation, and Infrastructure, SDG10—Reducing Inequalities, SDG13—Climate Action, and SDG14—Protecting Marine Life. At the same time, the INE has been monitoring European initiatives in a framework of cooperation with the United Nations Economic Commission for Europe (UNECE) and Eurostat in developing global indicators. In this context, we note a differentiated situation in terms of methodological stabilization and availability of these indicators, according to the classification system defined by the Inter-agency Expert Group (IAEG-SDG) ^[38].

This process has enabled national and international mapping of available information and identified the most appropriate sources of indicators for the monitoring of the 17 SDGs in Portugal. Figure 1 shows information and data availability in terms of indicators to support SDG achievements in Portugal, published in a national report on the implementation of the 2030 Agenda (in the graphic are % of indicators) ^[38].

Figure 1. Quantity of information and data availability for each SDG (in percentage). Source: Ministry of Foreign Affairs National Report on the Implementation of the 2030 Agenda for Sustainable Development ^[38].

In terms of indicators to be developed, the Instituto Nacional de Estatísticas—INE— highlights more attention to SDG 2, 5, 11, and 14, where there are a higher percentage of inconclusive data availability, together with SDG 10, 11, 12, 13, 14, 15, and 17 that have indicators out of scope to measure the evolution of these SDGs. These SDGs reflect a data disaggregation to map the progress of SDGs in the various population sectors, which have received more attention from Portugal’s authorities.

To focus all the existing information on a single platform, the INE has made available on its portal (www.ine.pt; accessed on 10 October 2021) a file on the “Sustainable Development Goals” (in continuous updating) to allow all interested users an easy overview of SDG indicators. In the context of international cooperation, the INE has also been supporting the Portuguese-speaking countries in developing their national statistical systems in the context of the Community of Portuguese Language Countries (CPLP). In this sense, the statistical cooperation has been, since the 1980s, one of the priorities of INE and of the Portuguese Cooperation—today, meeting objectives in SDG 17. The existing cooperation programs will reflect the new information needs, with particular emphasis on data disaggregation to reflect progress in the various sectors of the population, including the most vulnerable ^[38].

References

Palomares, I.; Martínez-Cámara, E.; Montes, R.; García-Moral, P.; Chiachio, M.; Chiachio, J.; Alonso, S.; Melero, F.J.; Molina, D.; Fernández, B.; et al. A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: Progress and prospects. Appl. Intell. 2021, 51, 6497–6527.
Goralski, M.A.; Tan, T.K. Artificial intelligence and sustainable development. Int. J. Manag. Educ. 2019, 18, 100330.
Alonso, S.; Montes, R.; Molina, D.; Palomares, I.; Martínez-Cámara, E.; Chiachio, M.; Chiachio, J.; Melero, F.; García-Moral, P.; Fernández, B.; et al. Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys. Sustainability 2021, 13, 6038.
United Nations. Transforming Our World: The 2030 Agenda for Sustainable Development; UN General Assembly, United Nations: New York, NY, USA, 2015; Available online: https://sdgs.un.org/2030agenda (accessed on 1 October 2021).
Sachs, J.D. The Age of Sustainable Development; Columbia University Press: New York, NY, USA, 2015; ISBN 978-0-231-17314-8.
Neto, G.C.D.O.; Correia, J.M.F.; Silva, P.C.; Sanches, A.G.D.O.; Lucato, W.C. Cleaner Production in the textile industry and its relationship to sustainable development goals. J. Clean. Prod. 2019, 228, 1514–1525.
Neto, G.C.D.O.; Tucci, H.N.P.; Correia, J.M.F.; Da Silva, P.C.; Da Silva, V.H.C.; Ganga, G.M.D. Assessing the implementation of Cleaner Production and company sizes: Survey in textile companies. J. Eng. Fibers Fabr. 2020, 15.
da Silva, P.C.; Neto, G.C.D.O.; Correia, J.M.F.; Tucci, H.N.P. Evaluation of economic, environmental, and operational performance of the adoption of cleaner production: Survey in large textile industries. J. Clean. Prod. 2020, 278, 123855.
Leite, R.; Amorim, M.; Rodrigues, M.; Neto, G.O. Overcoming Barriers for Adopting Cleaner Production: A Case Study in Brazilian Small Metal-Mechanic Companies. Sustainability 2019, 11, 4808.
Munoz, J.M.; Naqvi, A. Business Strategy in the Artificial Intelligence Economy; Business Expert Press: New York, NY, USA, 2018; ISBN 978-1-948198-98-1.
Klarin, A. The decade-long cryptocurrencies and the blockchain rollercoaster: Mapping the intellectual structure and charting future directions. Res. Int. Bus. Financ. 2019, 51, 101067.
McCarthy, J.; Minsky, M.L.; Rochester, N.; Shannon, C.E. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, 31 August 1955. AI Mag. 2006, 27, 12.
Floridi, L. What the Near Future of Artificial Intelligence Could Be. Philos. Technol. 2019, 32, 1–15.
Vinuesa, R.; Azizpour, H.; Leite, I.; Balaam, M.; Dignum, V.; Domisch, S.; Felländer, A.; Langhans, S.D.; Tegmark, M.; Nerini, F.F. The role of artificial intelligence in achieving the Sustainable Development Goals. Nat. Commun. 2020, 11, 1–10.
Wu, J.; Guo, S.; Huang, H.; Liu, W.; Xiang, Y. Information and Communications Technologies for Sustainable Development Goals: State-of-the-Art, Needs and Perspectives. IEEE Commun. Surv. Tutor. 2018, 20, 2389–2406.
Kamble, S.S.; Gunasekaran, A.; Sharma, R. Modeling the blockchain enabled traceability in agriculture supply chain. Int. J. Inf. Manag. 2019, 52, 101967.
Lin, J.; Shen, Z.; Zhang, A.; Chai, Y. Blockchain and IoT Based Food Traceability for Smart Agriculture. In Proceedings of the 3rd International Conference on Crowd Science and Engineering, Singapore, 28–31 July 2018; Volume 3, pp. 1–6.
Shafiee-Jood, M.; Cai, X. Reducing Food Loss and Waste to Enhance Food Security and Environmental Sustainability. Environ. Sci. Technol. 2016, 50, 8432–8443.
Kummitha, R.K.R. Smart technologies for fighting pandemics: The techno- and human- driven approaches in controlling the virus transmission. Gov. Inf. Q. 2020, 37, 101481.
Marconcini, M.; Metz, A.; Zeidler, J.; Esch, T. Urban Monitoring in Support of Sustainable Cities. In Proceedings of the 2015 Joint Urban Remote Sensing Event (JURSE), Lausanne, Switzerland, 30 March–1 April 2015; pp. 1–4.
Villagra, A.; Alba, E.; Luque, G. A better understanding on traffic light scheduling: New cellular GAs and new in-depth analysis of solutions. J. Comput. Sci. 2020, 41, 101085.
Vilajosana, I.; Llosa, J.; Martinez, B.; Domingo-Prieto, M.; Angles, A.; Vilajosana, X. Bootstrapping smart cities through a self-sustainable model based on big data flows. IEEE Commun. Mag. 2013, 51, 128–134.
Lima, M.S.M.; Delen, D. Predicting and explaining corruption across countries: A machine learning approach. Gov. Inf. Q. 2020, 37, 101407.
Anderson, K.; Ryan, B.; Sonntag, W.; Kavvada, A.; Friedl, L. Earth observation in service of the 2030 Agenda for Sustainable Development. Geo-Spat. Inf. Sci. 2017, 20, 77–96.
Noriega, M. The application of artificial intelligence in police interrogations: An analysis addressing the proposed effect AI has on racial and gender bias, cooperation, and false confessions. Futures 2020, 117, 102510.
Pan, X.; Song, M.L.; Zhang, J.; Zhou, G. Innovation network, technological learning and innovation performance of high-tech cluster enterprises. J. Knowl. Manag. 2019, 23, 1729–1746.
Zhou, J.; Riekki, J.; Hämäläinen, M.; Mattila, P.; Yu, X.; Liu, X.; Zhang, W. China-Finland Educloud Platform towards Innovative Education. In Image and Video Technology; PSIVT 2017. Lecture Notes in Computer, Science; Satoh, S., Ed.; Springer: Cham, Switzerland, 2017; pp. 172–185.
Tinelli, S.; Juran, I. Artificial intelligence-based monitoring system of water quality parameters for early detection of non-specific bio-contamination in water distribution systems. Water Supply 2019, 19, 1785–1792.
Kim, S.-K.; Huh, J.-H. Blockchain of Carbon Trading for UN Sustainable Development Goals. Sustainability 2020, 12, 4021.
Moro, A.; Holzer, A. A Framework to Predict Consumption Sustainability Levels of Individuals. Sustainability 2020, 12, 1423.
Salame, C.W.; Queiroz, J.C.B.; Rocha, G.D.M.; Amin, M.M. Mapping the Risk of Burning in the Brazilian Amazon with the Use of Logistic Regression and Fuzzy Inference. Math. Geol. 2012, 44, 241–256.
Exbrayat, J.-F.; Williams, M. Quantifying the net contribution of the historical Amazonian deforestation to climate change. Geophys. Res. Lett. 2015, 42, 2968–2976.
Avio, C.G.; Gorbi, S.; Regoli, F. Plastics and microplastics in the oceans: From emerging pollutants to emerged threat. Mar. Environ. Res. 2017, 128, 2–11.
Kako, S.; Morita, S.; Taneda, T. Estimation of plastic marine debris volumes on beaches using unmanned aerial vehicles and image processing based on deep learning. Mar. Pollut. Bull. 2020, 155, 111127.
Wolfert, S.; Ge, L.; Verdouw, C.; Bogaardt, M.-J. Big Data in Smart Farming—A review. Agric. Syst. 2017, 153, 69–80.
Ali, S.; Hussain, T.; Zhang, G.; Nurunnabi, M.; Li, B. The Implementation of Sustainable Development Goals in “BRICS” Countries. Sustainability 2018, 10, 2513.
Secretariat of Government of the Presidency of the Republic. Voluntary National Review on the Sustainable Development Goals—BRAZIL 2017. Available online: https://sustainabledevelopment.un.org/memberstates/brazil (accessed on 1 October 2021).
Ministry of Foreign Affairs. National Report on the Implementation of the 2030 Agenda for Sustainable Development PORTUGAL 2017. Available online: https://sustainabledevelopment.un.org/memberstates/portugal (accessed on 1 October 2021).

© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY)license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Upload a video for this entry

Information

Subjects: Computer Science, Artificial Intelligence

Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Angélica Pigola

View Times: 385

Update Date: 28 Mar 2022

Table of Contents

Video Upload Options

Confirm